Pandas Profiling Report

Dataset statistics

Number of variables	16
Number of observations	1836
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	217.2 KiB
Average record size in memory	121.1 B

Variable types

DateTime	1
Categorical	4
Numeric	11

Warnings

`username` has constant value "Benioff"	Constant
`cashtags` has constant value "0"	Constant
`tweet` has a high cardinality: 1836 distinct values	High cardinality
`mentions` is highly correlated with `number of tweets`	High correlation
`video` is highly correlated with `photos` and 1 other fields	High correlation
`photos` is highly correlated with `video`	High correlation
`urls` is highly correlated with `number of tweets`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`number of tweets` is highly correlated with `mentions` and 2 other fields	High correlation
`mentions` is highly correlated with `number of tweets`	High correlation
`video` is highly correlated with `photos` and 1 other fields	High correlation
`photos` is highly correlated with `video`	High correlation
`urls` is highly correlated with `number of tweets`	High correlation
`replies_count` is highly correlated with `retweets_count` and 2 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 2 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 2 other fields	High correlation
`number of tweets` is highly correlated with `mentions` and 5 other fields	High correlation
`video` is highly correlated with `photos`	High correlation
`photos` is highly correlated with `video`	High correlation
`urls` is highly correlated with `number of tweets`	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`number of tweets` is highly correlated with `urls`	High correlation
`urls` is highly correlated with `mentions` and 1 other fields	High correlation
`replies_count` is highly correlated with `retweets_count` and 1 other fields	High correlation
`video` is highly correlated with `photos` and 1 other fields	High correlation
`mentions` is highly correlated with `urls` and 2 other fields	High correlation
`retweets_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`photos` is highly correlated with `video` and 2 other fields	High correlation
`bins` is highly correlated with `percent change`	High correlation
`number of tweets` is highly correlated with `urls` and 3 other fields	High correlation
`likes_count` is highly correlated with `replies_count` and 1 other fields	High correlation
`percent change` is highly correlated with `bins`	High correlation
`cashtags` is highly correlated with `bins` and 1 other fields	High correlation
`bins` is highly correlated with `cashtags` and 1 other fields	High correlation
`username` is highly correlated with `cashtags` and 1 other fields	High correlation
`tweet` is uniformly distributed	Uniform
`date` has unique values	Unique
`tweet` has unique values	Unique
`mentions` has 572 (31.2%) zeros	Zeros
`hashtags` has 1582 (86.2%) zeros	Zeros
`video` has 698 (38.0%) zeros	Zeros
`photos` has 819 (44.6%) zeros	Zeros
`urls` has 511 (27.8%) zeros	Zeros
`replies_count` has 50 (2.7%) zeros	Zeros
`retweets_count` has 28 (1.5%) zeros	Zeros
`percent change` has 19 (1.0%) zeros	Zeros

Reproduction

Analysis started	2021-09-27 19:03:51.187263
Analysis finished	2021-09-27 19:04:08.436977
Duration	17.25 seconds
Software version	pandas-profiling v3.0.0
Download configuration	config.json

date
Date

UNIQUE

Distinct	1836
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	14.5 KiB

Minimum	2016-08-23 09:30:00
Maximum	2021-05-28 09:30:00

Histogram

Histogram with fixed size bins (bins=50)

tweet
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	1836
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	14.5 KiB

All docs, code, & packing of the Atari games I wrote at 16 &17 to put myself through @USC! https://t.co/dNVx6vtTwr https://t.co/O7vXYPWX1O	1
Who would have thought we could raise $11M+ in one concert for @UCSFChildrens @UCSFBenioffOAK. $81M raising since 2010. Thank you Ohana!!! We love you so much!❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️ https://t.co/mn8grAeELb	1
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/IpXxC6frzw Dreamforce 2019 #DF19 https://t.co/faEVnJTxin Parker’s live behind the scenes tour of Dreamforce! https://t.co/smzDPnMUZK See the Free Mural @SFMOMA. “The mural, which @JRart calls a living painting, has more than 1200 people, incl local leaders @GavinNewsom, @CleveJones1, @Warriors @Money23Green, + members of the San Francisco Gay Men’s Chorus (@SFGMC)." -@hyperallergic https://t.co/bLym3gdqDF Calming down with the @thichnhathanh Monastics before Dreamforce. DF Attendees book a session with the Monastics on the Dreamforce App & learn how to meditate & breath. Excited to have the Monastics back for the 5th year! We even built a mini @plumvillageom this year!☮️ https://t.co/NaWeneorjX	1
Even if you aren’t at Dreamforce in person this year, you can watch and participate online. Tap “Set Reminder” below to get notified when the keynote starts and watch live! https://t.co/LpYULckps9 All of us need to give the Warriors a hand during these times. ❤️🏀 https://t.co/415erypha0	1
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/BgRYWLLqLh	1
Other values (1831)	1831

Length

Max length	4908
Median length	293
Mean length	450.672658
Min length	11

Characters and Unicode

Total characters	827435
Distinct characters	304
Distinct categories	20 ?
Distinct scripts	5 ?
Distinct blocks	18 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1836 ?
Unique (%)	100.0%

Sample

1st row	All docs, code, & packing of the Atari games I wrote at 16 &17 to put myself through @USC! https://t.co/dNVx6vtTwr https://t.co/O7vXYPWX1O
2nd row	Congratulations @Salesforce 6 years as one of The World's Most Innovative Companies - Forbes - This year #2! https://t.co/YvyXpclp7B Incredible to be at @plumvillageom. Such a magical place. https://t.co/evqaDhPDHA Wonderful to be with Zen Master @thichnhathanh in @plumvillageom --incredible rebirth of health strength & energy. https://t.co/SPvrJjbdPU Thank you @alexrkonrad Welcome Salesforce Einstein---1st comprehensive AI platform for CRM. https://t.co/MrUk3m4SlV https://t.co/4qz6CXphOn All of my thoughts and prayers are with the people of Italy. May all the residents be protected and safe. Gd Bless. https://t.co/hefc0Fllng To all the friends of @thichnhathanh: I met with the venerable mindfulness teacher today and he was as strong & capable as I have seen him. Salesforce has issued its stakeholder report. We need feedback from all stakeholders on how to move forward. https://t.co/1MOiSl7Jml
3rd row	"Would it be such a bad thing if doing business to change the world became the new normal?" @PledgeOne https://t.co/X2ZN72WdGU Thank you @greentechlady for writing in @FortuneMagazine on @PledgeOne and our 1-1-1 model. https://t.co/kgF8C6TU8R Wonderful to take three days of mindfulness with @thichnhathanh and the monastics at @plumvillageom. Coming home. https://t.co/LyiY521IxZ Thank you @greentechlady and @FortuneMagazine for introducing Salesforce Einstein. https://t.co/tr4V0Dd3fq Thank you @POTUS @McCauley_Lab @SenBrianSchatz @OceanElders for expanding #Papahanaumokuakea! https://t.co/EJAKhANQhq Mahalo Malama Aina!
4th row	Thank you @Lilly_Ledbetter! https://t.co/expO7Ti9RQ #WomensEqualityDay Thank you @PattyArquette!
5th row	Wake up call from @PMaurerICRC ceo @ICRC now 65M refugees/24 new refugees every minute. How can we help them? https://t.co/OQAgzJKJau Very excited to get to @Dreamforce and reveal our vision for Einstein---our trusted comprehensive AI platform. https://t.co/ndUhNN9xxx

Common Values

Value	Count	Frequency (%)
All docs, code, & packing of the Atari games I wrote at 16 &17 to put myself through @USC! https://t.co/dNVx6vtTwr https://t.co/O7vXYPWX1O	1	0.1%
Who would have thought we could raise $11M+ in one concert for @UCSFChildrens @UCSFBenioffOAK. $81M raising since 2010. Thank you Ohana!!! We love you so much!❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️❤️ https://t.co/mn8grAeELb	1	0.1%
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/IpXxC6frzw Dreamforce 2019 #DF19 https://t.co/faEVnJTxin Parker’s live behind the scenes tour of Dreamforce! https://t.co/smzDPnMUZK See the Free Mural @SFMOMA. “The mural, which @JRart calls a living painting, has more than 1200 people, incl local leaders @GavinNewsom, @CleveJones1, @Warriors @Money23Green, + members of the San Francisco Gay Men’s Chorus (@SFGMC)." -@hyperallergic https://t.co/bLym3gdqDF Calming down with the @thichnhathanh Monastics before Dreamforce. DF Attendees book a session with the Monastics on the Dreamforce App & learn how to meditate & breath. Excited to have the Monastics back for the 5th year! We even built a mini @plumvillageom this year!☮️ https://t.co/NaWeneorjX	1	0.1%
Even if you aren’t at Dreamforce in person this year, you can watch and participate online. Tap “Set Reminder” below to get notified when the keynote starts and watch live! https://t.co/LpYULckps9 All of us need to give the Warriors a hand during these times. ❤️🏀 https://t.co/415erypha0	1	0.1%
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/BgRYWLLqLh	1	0.1%
Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/AM7s2viPxk Salesforce Tower Tonight. Photo Credit: L.Kessler https://t.co/6pq2vX2yJ1	1	0.1%
Riveting @maggierogers at the Time 100 Next! https://t.co/ab5a0uTdms Dreamforce. 27 Keynote Soeakers. 2700 Sessions. 171,000 attendees. 13,000,000 online. 2M Trailblazers. Tim Cook. President Barack Obama. Mother of Dragons Emilia Clark. Megan Rapinoe. Fleetwood Mac. Thich Nhat Hahn Monastics. Deepak Chopra. Customer 360. Tableau. & Much More. https://t.co/R05cUHc8dR	1	0.1%
Thank you Andrew! ❤️ @andrewrsorkin https://t.co/00iaffMoZq	1	0.1%
Dreamforce. 27 Keynote Soeakers. 2200 Sessions. 150,000 attendees. 10,000,000 online. 2M Trailblazers. Fleetwood Mac. https://t.co/R6OEd3osZ3 Once again @TeamTime crushes it with Time 100 Next. So proud of them! ⚡️ #TIME100Next: Spotlighting 100 rising stars who are shaping the future of their fields. https://t.co/QCZxuixm19	1	0.1%
I am with my #datafam at the one and only @tableau conference! Amazing energy. https://t.co/1Vb3RZIKE4 Join us! @awscloud, @Genesys, & Salesforce come together with @linuxfoundation to standardize interoperability across cloud apps. Thanks @btaylor for your leadership! All partners welcome to join the open source CIM. https://t.co/bgMXARPRs4	1	0.1%
Other values (1826)	1826	99.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	4608	3.9%
to	3555	3.0%
of	2496	2.1%
amp	1979	1.7%
for	1853	1.6%
a	1776	1.5%
in	1690	1.4%
and	1580	1.3%
is	1343	1.1%
you	1276	1.1%
Other values (16693)	96710	81.4%

Most occurring characters

Value	Count	Frequency (%)
	125395	15.2%
e	63236	7.6%
t	55133	6.7%
o	50153	6.1%
a	45824	5.5%
s	39915	4.8%
i	37568	4.5%
n	36786	4.4%
r	35987	4.3%
h	26722	3.2%
Other values (294)	310716	37.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	574769	69.5%
Space Separator	125443	15.2%
Uppercase Letter	54981	6.6%
Other Punctuation	47830	5.8%
Decimal Number	17057	2.1%
Final Punctuation	1477	0.2%
Other Symbol	1333	0.2%
Dash Punctuation	1158	0.1%
Format	916	0.1%
Currency Symbol	572	0.1%
Other values (10)	1899	0.2%

Most frequent character per category

Other Symbol

Value	Count	Frequency (%)
❤	490	36.8%
😷	86	6.5%
🇸	39	2.9%
🇺	35	2.6%
🗽	34	2.6%
👍	31	2.3%
🌲	29	2.2%
🇮	25	1.9%
🇳	24	1.8%
🇵	18	1.4%
Other values (170)	522	39.2%

Lowercase Letter

Value	Count	Frequency (%)
e	63236	11.0%
t	55133	9.6%
o	50153	8.7%
a	45824	8.0%
s	39915	6.9%
i	37568	6.5%
n	36786	6.4%
r	35987	6.3%
h	26722	4.6%
l	24463	4.3%
Other values (20)	158982	27.7%

Uppercase Letter

Value	Count	Frequency (%)
T	4475	8.1%
S	4423	8.0%
C	3652	6.6%
I	2957	5.4%
A	2843	5.2%
M	2811	5.1%
W	2439	4.4%
F	2387	4.3%
O	2374	4.3%
P	2233	4.1%
Other values (17)	24387	44.4%

Other Punctuation

Value	Count	Frequency (%)
/	14233	29.8%
.	12380	25.9%
:	5197	10.9%
@	4944	10.3%
,	2978	6.2%
!	2207	4.6%
;	2048	4.3%
&	2025	4.2%
#	536	1.1%
'	465	1.0%
Other values (9)	817	1.7%

Decimal Number

Value	Count	Frequency (%)
0	3309	19.4%
1	2906	17.0%
2	2212	13.0%
5	1444	8.5%
4	1294	7.6%
3	1262	7.4%
9	1257	7.4%
7	1206	7.1%
8	1099	6.4%
6	1067	6.3%

Math Symbol

Value	Count	Frequency (%)
+	36	44.4%
=	30	37.0%
\|	13	16.0%
~	1	1.2%
→	1	1.2%

Format

Value	Count	Frequency (%)
⁦	394	43.0%
⁩	359	39.2%
	146	15.9%
‍	15	1.6%
⁠	2	0.2%

Space Separator

Value	Count	Frequency (%)
	125395	> 99.9%
	46	< 0.1%
	1	< 0.1%
	1	< 0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	865	74.7%
—	273	23.6%
–	20	1.7%

Currency Symbol

Value	Count	Frequency (%)
$	570	99.7%
£	1	0.2%
₹	1	0.2%

Connector Punctuation

Value	Count	Frequency (%)
_	335	96.8%
＿	11	3.2%

Final Punctuation

Value	Count	Frequency (%)
’	1079	73.1%
”	398	26.9%

Close Punctuation

Value	Count	Frequency (%)
)	220	97.3%
]	6	2.7%

Open Punctuation

Value	Count	Frequency (%)
(	177	96.7%
[	6	3.3%

Initial Punctuation

Value	Count	Frequency (%)
“	426	97.0%
‘	13	3.0%

Modifier Symbol

Value	Count	Frequency (%)
￣	11	50.0%
🏽	11	50.0%

Other Letter

Value	Count	Frequency (%)
ㅅ	1	50.0%
づ	1	50.0%

Nonspacing Mark

Value	Count	Frequency (%)
️	569	100.0%

Modifier Letter

Value	Count	Frequency (%)
ʻ	1	100.0%

Control

Value	Count	Frequency (%)
	30	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	629750	76.1%
Common	197099	23.8%
Inherited	584	0.1%
Hangul	1	< 0.1%
Hiragana	1	< 0.1%

Most frequent character per script

Common

Value	Count	Frequency (%)
	125395	63.6%
/	14233	7.2%
.	12380	6.3%
:	5197	2.6%
@	4944	2.5%
0	3309	1.7%
,	2978	1.5%
1	2906	1.5%
2	2212	1.1%
!	2207	1.1%
Other values (233)	21338	10.8%

Latin

Value	Count	Frequency (%)
e	63236	10.0%
t	55133	8.8%
o	50153	8.0%
a	45824	7.3%
s	39915	6.3%
i	37568	6.0%
n	36786	5.8%
r	35987	5.7%
h	26722	4.2%
l	24463	3.9%
Other values (47)	213963	34.0%

Inherited

Value	Count	Frequency (%)
️	569	97.4%
‍	15	2.6%

Hangul

Value	Count	Frequency (%)
ㅅ	1	100.0%

Hiragana

Value	Count	Frequency (%)
づ	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	822285	99.4%
Punctuation	3007	0.4%
VS	569	0.1%
Dingbats	537	0.1%
None	423	0.1%
Enclosed Alphanum Sup	221	< 0.1%
Latin 1 Sup	199	< 0.1%
Emoticons	127	< 0.1%
Misc Symbols	53	< 0.1%
Misc Technical	4	< 0.1%
Other values (8)	10	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	125395	15.2%
e	63236	7.7%
t	55133	6.7%
o	50153	6.1%
a	45824	5.6%
s	39915	4.9%
i	37568	4.6%
n	36786	4.5%
r	35987	4.4%
h	26722	3.2%
Other values (80)	305566	37.2%

Punctuation

Value	Count	Frequency (%)
’	1079	35.9%
“	426	14.2%
”	398	13.2%
⁦	394	13.1%
⁩	359	11.9%
—	273	9.1%
–	20	0.7%
…	20	0.7%
‍	15	0.5%
‘	13	0.4%
Other values (5)	10	0.3%

Enclosed Alphanum Sup

Value	Count	Frequency (%)
🇸	39	17.6%
🇺	35	15.8%
🇮	25	11.3%
🇳	24	10.9%
🇵	18	8.1%
🇯	14	6.3%
🇬	11	5.0%
🇫	11	5.0%
🇷	8	3.6%
🇻	7	3.2%
Other values (10)	29	13.1%

Dingbats

Value	Count	Frequency (%)
❤	490	91.2%
❌	13	2.4%
✂	9	1.7%
➡	6	1.1%
✅	5	0.9%
✓	4	0.7%
✈	3	0.6%
✌	2	0.4%
✔	2	0.4%
✨	2	0.4%

VS

Value	Count	Frequency (%)
️	569	100.0%

None

Value	Count	Frequency (%)
🗽	34	8.0%
👍	31	7.3%
🌲	29	6.9%
👽	17	4.0%
🤘	16	3.8%
￣	11	2.6%
＿	11	2.6%
🏽	11	2.6%
💻	11	2.6%
👩	11	2.6%
Other values (112)	241	57.0%

Misc Symbols

Value	Count	Frequency (%)
⚒	13	24.5%
⚔	8	15.1%
⚡	6	11.3%
♥	4	7.5%
♠	3	5.7%
♣	3	5.7%
♦	3	5.7%
☁	3	5.7%
☀	3	5.7%
☮	2	3.8%
Other values (4)	5	9.4%

Latin 1 Sup

Value	Count	Frequency (%)
	146	73.4%
	46	23.1%
ç	2	1.0%
Ì	2	1.0%
á	1	0.5%
£	1	0.5%
°	1	0.5%

Letterlike Symbols

Value	Count	Frequency (%)
™	1	100.0%

Emoticons

Value	Count	Frequency (%)
😷	86	67.7%
🙏	11	8.7%
😂	10	7.9%
😇	7	5.5%
😎	6	4.7%
😱	2	1.6%
🙀	1	0.8%
😘	1	0.8%
🙌	1	0.8%
😜	1	0.8%

Latin Ext A

Value	Count	Frequency (%)
ā	2	66.7%
ū	1	33.3%

Modifier Letters

Value	Count	Frequency (%)
ʻ	1	100.0%

Compat Jamo

Value	Count	Frequency (%)
ㅅ	1	100.0%

Hiragana

Value	Count	Frequency (%)
づ	1	100.0%

Arrows

Value	Count	Frequency (%)
→	1	100.0%

Misc Technical

Value	Count	Frequency (%)
⏩	1	25.0%
⏭	1	25.0%
⏰	1	25.0%
⏱	1	25.0%

Math Alphanum

Value	Count	Frequency (%)
𝟬	1	100.0%

Currency Symbols

Value	Count	Frequency (%)
₹	1	100.0%

username
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	14.5 KiB

Benioff	1836

Length

Max length	7
Median length	7
Mean length	7
Min length	7

Characters and Unicode

Total characters	12852
Distinct characters	6
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Benioff
2nd row	Benioff
3rd row	Benioff
4th row	Benioff
5th row	Benioff

Common Values

Value	Count	Frequency (%)
Benioff	1836	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
benioff	1836	100.0%

Most occurring characters

Value	Count	Frequency (%)
f	3672	28.6%
B	1836	14.3%
e	1836	14.3%
n	1836	14.3%
i	1836	14.3%
o	1836	14.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	11016	85.7%
Uppercase Letter	1836	14.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
f	3672	33.3%
e	1836	16.7%
n	1836	16.7%
i	1836	16.7%
o	1836	16.7%

Uppercase Letter

Value	Count	Frequency (%)
B	1836	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	12852	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
f	3672	28.6%
B	1836	14.3%
e	1836	14.3%
n	1836	14.3%
i	1836	14.3%
o	1836	14.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	12852	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
f	3672	28.6%
B	1836	14.3%
e	1836	14.3%
n	1836	14.3%
i	1836	14.3%
o	1836	14.3%

mentions
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	22
Distinct (%)	1.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.089869281

Minimum	0
Maximum	36
Zeros	572
Zeros (%)	31.2%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	3
95-th percentile	7
Maximum	36
Range	36
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.793352519
Coefficient of variation (CV)	1.336615904
Kurtosis	22.63772032
Mean	2.089869281
Median Absolute Deviation (MAD)	1
Skewness	3.470487506
Sum	3837
Variance	7.802818294
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=22)

Value	Count	Frequency (%)
0	572	31.2%
1	435	23.7%
2	292	15.9%
3	174	9.5%
4	126	6.9%
5	82	4.5%
6	48	2.6%
7	27	1.5%
8	25	1.4%
9	16	0.9%
Other values (12)	39	2.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	572	31.2%
1	435	23.7%
2	292	15.9%
3	174	9.5%
4	126	6.9%
5	82	4.5%
6	48	2.6%
7	27	1.5%
8	25	1.4%
9	16	0.9%

Value	Count	Frequency (%)
36	1	0.1%
27	1	0.1%
24	1	0.1%
19	2	0.1%
18	3	0.2%
17	2	0.1%
16	2	0.1%
15	4	0.2%
13	2	0.1%
12	6	0.3%

hashtags
Real number (ℝ_≥0)

ZEROS

Distinct	8
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.197167756

Minimum	0
Maximum	7
Zeros	1582
Zeros (%)	86.2%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	1
Maximum	7
Range	7
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.5787448941
Coefficient of variation (CV)	2.935291784
Kurtosis	26.07480635
Mean	0.197167756
Median Absolute Deviation (MAD)	0
Skewness	4.228184394
Sum	362
Variance	0.3349456525
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=8)

Value	Count	Frequency (%)
0	1582	86.2%
1	178	9.7%
2	56	3.1%
3	14	0.8%
4	3	0.2%
5	1	0.1%
7	1	0.1%
6	1	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1582	86.2%
1	178	9.7%
2	56	3.1%
3	14	0.8%
4	3	0.2%
5	1	0.1%
6	1	0.1%
7	1	0.1%

Value	Count	Frequency (%)
7	1	0.1%
6	1	0.1%
5	1	0.1%
4	3	0.2%
3	14	0.8%
2	56	3.1%
1	178	9.7%
0	1582	86.2%

cashtags
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	14.5 KiB

0	1836

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	1836
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	1836	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	1836	100.0%

Most occurring characters

Value	Count	Frequency (%)
0	1836	100.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	1836	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	1836	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	1836	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	1836	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1836	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	1836	100.0%

video
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	10
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.088779956

Minimum	0
Maximum	9
Zeros	698
Zeros (%)	38.0%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	4
Maximum	9
Range	9
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.271041725
Coefficient of variation (CV)	1.167400003
Kurtosis	4.865854915
Mean	1.088779956
Median Absolute Deviation (MAD)	1
Skewness	1.844915635
Sum	1999
Variance	1.615547067
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=10)

Value	Count	Frequency (%)
0	698	38.0%
1	667	36.3%
2	260	14.2%
3	114	6.2%
4	52	2.8%
5	22	1.2%
6	15	0.8%
7	4	0.2%
9	2	0.1%
8	2	0.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	698	38.0%
1	667	36.3%
2	260	14.2%
3	114	6.2%
4	52	2.8%
5	22	1.2%
6	15	0.8%
7	4	0.2%
8	2	0.1%
9	2	0.1%

Value	Count	Frequency (%)
9	2	0.1%
8	2	0.1%
7	4	0.2%
6	15	0.8%
5	22	1.2%
4	52	2.8%
3	114	6.2%
2	260	14.2%
1	667	36.3%
0	698	38.0%

photos
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	15
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.273965142

Minimum	0
Maximum	20
Zeros	819
Zeros (%)	44.6%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	5
Maximum	20
Range	20
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.879937899
Coefficient of variation (CV)	1.475658821
Kurtosis	11.09279374
Mean	1.273965142
Median Absolute Deviation (MAD)	1
Skewness	2.632929816
Sum	2339
Variance	3.534166503
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=15)

Value	Count	Frequency (%)
0	819	44.6%
1	539	29.4%
2	171	9.3%
4	103	5.6%
3	83	4.5%
5	53	2.9%
6	24	1.3%
8	18	1.0%
7	9	0.5%
9	9	0.5%
Other values (5)	8	0.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	819	44.6%
1	539	29.4%
2	171	9.3%
3	83	4.5%
4	103	5.6%
5	53	2.9%
6	24	1.3%
7	9	0.5%
8	18	1.0%
9	9	0.5%

Value	Count	Frequency (%)
20	1	0.1%
15	1	0.1%
13	1	0.1%
11	2	0.1%
10	3	0.2%
9	9	0.5%
8	18	1.0%
7	9	0.5%
6	24	1.3%
5	53	2.9%

urls
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	13
Distinct (%)	0.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	1.468409586

Minimum	0
Maximum	16
Zeros	511
Zeros (%)	27.8%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	1
Q3	2
95-th percentile	5
Maximum	16
Range	16
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	1.615518704
Coefficient of variation (CV)	1.100182619
Kurtosis	8.760230291
Mean	1.468409586
Median Absolute Deviation (MAD)	1
Skewness	2.272535728
Sum	2696
Variance	2.609900684
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=13)

Value	Count	Frequency (%)
1	665	36.2%
0	511	27.8%
2	345	18.8%
3	147	8.0%
4	72	3.9%
5	39	2.1%
6	26	1.4%
7	15	0.8%
8	5	0.3%
10	4	0.2%
Other values (3)	7	0.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	511	27.8%
1	665	36.2%
2	345	18.8%
3	147	8.0%
4	72	3.9%
5	39	2.1%
6	26	1.4%
7	15	0.8%
8	5	0.3%
9	3	0.2%

Value	Count	Frequency (%)
16	1	0.1%
11	3	0.2%
10	4	0.2%
9	3	0.2%
8	5	0.3%
7	15	0.8%
6	26	1.4%
5	39	2.1%
4	72	3.9%
3	147	8.0%

replies_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	196
Distinct (%)	10.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	34.74618736

Minimum	0
Maximum	1125
Zeros	50
Zeros (%)	2.7%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	6
median	15
Q3	36
95-th percentile	132
Maximum	1125
Range	1125
Interquartile range (IQR)	30

Descriptive statistics

Standard deviation	63.80391335
Coefficient of variation (CV)	1.836285308
Kurtosis	69.59140423
Mean	34.74618736
Median Absolute Deviation (MAD)	12
Skewness	6.321682872
Sum	63794
Variance	4070.939359
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2	92	5.0%
3	90	4.9%
1	76	4.1%
5	74	4.0%
6	70	3.8%
7	62	3.4%
4	62	3.4%
8	58	3.2%
12	54	2.9%
9	52	2.8%
Other values (186)	1146	62.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	50	2.7%
1	76	4.1%
2	92	5.0%
3	90	4.9%
4	62	3.4%
5	74	4.0%
6	70	3.8%
7	62	3.4%
8	58	3.2%
9	52	2.8%

Value	Count	Frequency (%)
1125	1	0.1%
811	1	0.1%
595	1	0.1%
503	1	0.1%
467	1	0.1%
436	1	0.1%
397	1	0.1%
385	1	0.1%
377	1	0.1%
367	1	0.1%

retweets_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	552
Distinct (%)	30.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	209.5174292

Minimum	0
Maximum	7239
Zeros	28
Zeros (%)	1.5%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	0
5-th percentile	7
Q1	37
median	95
Q3	230
95-th percentile	754.25
Maximum	7239
Range	7239
Interquartile range (IQR)	193

Descriptive statistics

Standard deviation	388.355864
Coefficient of variation (CV)	1.853573068
Kurtosis	101.6679045
Mean	209.5174292
Median Absolute Deviation (MAD)	72
Skewness	7.678635788
Sum	384674
Variance	150820.2771
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	28	1.5%
19	20	1.1%
24	19	1.0%
15	18	1.0%
18	17	0.9%
47	17	0.9%
22	15	0.8%
25	15	0.8%
16	15	0.8%
43	15	0.8%
Other values (542)	1657	90.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	28	1.5%
1	10	0.5%
2	10	0.5%
3	9	0.5%
4	11	0.6%
5	11	0.6%
6	12	0.7%
7	12	0.7%
8	9	0.5%
9	11	0.6%

Value	Count	Frequency (%)
7239	1	0.1%
6343	1	0.1%
3042	1	0.1%
3001	1	0.1%
2979	1	0.1%
2636	1	0.1%
2532	1	0.1%
2520	1	0.1%
2422	1	0.1%
2352	1	0.1%

likes_count
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	1088
Distinct (%)	59.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	904.9455338

Minimum	0
Maximum	16564
Zeros	4
Zeros (%)	0.2%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	0
5-th percentile	38
Q1	163.75
median	408.5
Q3	976.25
95-th percentile	3532.25
Maximum	16564
Range	16564
Interquartile range (IQR)	812.5

Descriptive statistics

Standard deviation	1529.980052
Coefficient of variation (CV)	1.690687445
Kurtosis	30.17048411
Mean	904.9455338
Median Absolute Deviation (MAD)	308.5
Skewness	4.658805838
Sum	1661480
Variance	2340838.961
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
98	8	0.4%
135	8	0.4%
172	7	0.4%
66	7	0.4%
111	7	0.4%
1	6	0.3%
204	6	0.3%
323	6	0.3%
775	6	0.3%
15	6	0.3%
Other values (1078)	1769	96.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	4	0.2%
1	6	0.3%
2	2	0.1%
3	3	0.2%
5	4	0.2%
6	5	0.3%
7	3	0.2%
8	2	0.1%
9	2	0.1%
10	3	0.2%

Value	Count	Frequency (%)
16564	1	0.1%
15906	1	0.1%
15064	1	0.1%
13983	1	0.1%
12699	1	0.1%
11637	1	0.1%
11532	1	0.1%
11324	1	0.1%
11237	1	0.1%
11200	1	0.1%

number of tweets
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	21
Distinct (%)	1.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2.575708061

Minimum	1
Maximum	31
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	1
median	2
Q3	3
95-th percentile	7
Maximum	31
Range	30
Interquartile range (IQR)	2

Descriptive statistics

Standard deviation	2.333090544
Coefficient of variation (CV)	0.9058055063
Kurtosis	22.42709342
Mean	2.575708061
Median Absolute Deviation (MAD)	1
Skewness	3.495708836
Sum	4729
Variance	5.443311487
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=21)

Value	Count	Frequency (%)
1	749	40.8%
2	441	24.0%
3	258	14.1%
4	147	8.0%
5	80	4.4%
6	57	3.1%
7	36	2.0%
8	20	1.1%
10	12	0.7%
9	10	0.5%
Other values (11)	26	1.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	749	40.8%
2	441	24.0%
3	258	14.1%
4	147	8.0%
5	80	4.4%
6	57	3.1%
7	36	2.0%
8	20	1.1%
9	10	0.5%
10	12	0.7%

Value	Count	Frequency (%)
31	1	0.1%
22	1	0.1%
21	1	0.1%
19	1	0.1%
18	1	0.1%
16	2	0.1%
15	2	0.1%
14	1	0.1%
13	2	0.1%
12	5	0.3%

price
Real number (ℝ_≥0)

Distinct	1731
Distinct (%)	94.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	145.58811

Minimum	66.58999634
Maximum	283.4700012
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	14.5 KiB

Quantile statistics

Minimum	66.58999634
5-th percentile	73.73000145
Q1	100.6999969
median	146.5650024
Q3	172.5175018
95-th percentile	246.1331234
Maximum	283.4700012
Range	216.8800049
Interquartile range (IQR)	71.81750488

Descriptive statistics

Standard deviation	51.71778934
Coefficient of variation (CV)	0.3552336062
Kurtosis	-0.5424760603
Mean	145.58811
Median Absolute Deviation (MAD)	39.43250275
Skewness	0.4728657302
Sum	267299.77
Variance	2674.729734
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
100.6999969	4	0.2%
154.5700073	3	0.2%
162	3	0.2%
224.8999939	3	0.2%
83.55999756	3	0.2%
160	3	0.2%
270	3	0.2%
94.94000244	3	0.2%
158.7599945	2	0.1%
83.94999695	2	0.1%
Other values (1721)	1807	98.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
66.58999634	1	0.1%
68.20999908	1	0.1%
68.256663	1	0.1%
68.41000366	1	0.1%
68.41999817	1	0.1%
68.45999908	1	0.1%
69.15000153	1	0.1%
69.29000092	1	0.1%
69.32500076	1	0.1%
69.34250069	1	0.1%

Value	Count	Frequency (%)
283.4700012	1	0.1%
276.3200073	1	0.1%
272.6499939	1	0.1%
272.1333313	1	0.1%
271	1	0.1%
270.8533325	1	0.1%
270.5799866	1	0.1%
270.4266663	1	0.1%
270	3	0.2%
267.6333313	1	0.1%

percent change
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	1818
Distinct (%)	99.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.0004603068847

Minimum	-0.06618367407
Maximum	0.1662115397
Zeros	19
Zeros (%)	1.0%
Negative	842
Negative (%)	45.9%
Memory size	14.5 KiB

Quantile statistics

Minimum	-0.06618367407
5-th percentile	-0.02267109011
Q1	-0.004871081758
median	0.0006092832829
Q3	0.006504909175
95-th percentile	0.02115178706
Maximum	0.1662115397
Range	0.2323952138
Interquartile range (IQR)	0.01137599093

Descriptive statistics

Standard deviation	0.01429250023
Coefficient of variation (CV)	31.049938
Kurtosis	14.01655829
Mean	0.0004603068847
Median Absolute Deviation (MAD)	0.005703514756
Skewness	0.7037985043
Sum	0.8451234403
Variance	0.0002042755628
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	19	1.0%
0.009074050221	1	0.1%
0.001955556865	1	0.1%
0.00534493515	1	0.1%
-0.007499973948	1	0.1%
0.006649584941	1	0.1%
-0.006039994213	1	0.1%
-0.004578350826	1	0.1%
0.003696632611	1	0.1%
0.00410915766	1	0.1%
Other values (1808)	1808	98.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-0.06618367407	1	0.1%
-0.06582162712	1	0.1%
-0.06575516874	1	0.1%
-0.06411861737	1	0.1%
-0.06085637252	1	0.1%
-0.05900001526	1	0.1%
-0.05830001831	1	0.1%
-0.05643277959	1	0.1%
-0.05619250806	1	0.1%
-0.05520205096	1	0.1%

Value	Count	Frequency (%)
0.1662115397	1	0.1%
0.08577696984	1	0.1%
0.06629919818	1	0.1%
0.06381537442	1	0.1%
0.06316242346	1	0.1%
0.06229685544	1	0.1%
0.06151081332	1	0.1%
0.0592923849	1	0.1%
0.05825238876	1	0.1%
0.0540086248	1	0.1%

bins
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

no change	1486
rise	178
drop	172

Length

Max length	9
Median length	9
Mean length	8.046840959
Min length	4

Characters and Unicode

Total characters	14774
Distinct characters	13
Distinct categories	2 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	no change
2nd row	no change
3rd row	no change
4th row	no change
5th row	no change

Common Values

Value	Count	Frequency (%)
no change	1486	80.9%
rise	178	9.7%
drop	172	9.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
no	1486	44.7%
change	1486	44.7%
rise	178	5.4%
drop	172	5.2%

Most occurring characters

Value	Count	Frequency (%)
n	2972	20.1%
e	1664	11.3%
o	1658	11.2%
	1486	10.1%
c	1486	10.1%
h	1486	10.1%
a	1486	10.1%
g	1486	10.1%
r	350	2.4%
i	178	1.2%
Other values (3)	522	3.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	13288	89.9%
Space Separator	1486	10.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	2972	22.4%
e	1664	12.5%
o	1658	12.5%
c	1486	11.2%
h	1486	11.2%
a	1486	11.2%
g	1486	11.2%
r	350	2.6%
i	178	1.3%
s	178	1.3%
Other values (2)	344	2.6%

Space Separator

Value	Count	Frequency (%)
	1486	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	13288	89.9%
Common	1486	10.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	2972	22.4%
e	1664	12.5%
o	1658	12.5%
c	1486	11.2%
h	1486	11.2%
a	1486	11.2%
g	1486	11.2%
r	350	2.6%
i	178	1.3%
s	178	1.3%
Other values (2)	344	2.6%

Common

Value	Count	Frequency (%)
	1486	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	14774	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	2972	20.1%
e	1664	11.3%
o	1658	11.2%
	1486	10.1%
c	1486	10.1%
h	1486	10.1%
a	1486	10.1%
g	1486	10.1%
r	350	2.4%
i	178	1.2%
Other values (3)	522	3.5%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	date	tweet	username	mentions	hashtags	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
0	2016-08-23 09:30:00	All docs, code, & packing of the Atari games I wrote at 16 &17 to put myself through @USC! https://t.co/dNVx6vtTwr https://t.co/O7vXYPWX1O	Benioff	1	0	1	4	1	12	34	160	1	77.820000	0.000000	no change
1	2016-08-24 09:30:00	Congratulations @Salesforce 6 years as one of The World's Most Innovative Companies - Forbes - This year #2! https://t.co/YvyXpclp7B Incredible to be at @plumvillageom. Such a magical place. https://t.co/evqaDhPDHA Wonderful to be with Zen Master @thichnhathanh in @plumvillageom --incredible rebirth of health strength & energy. https://t.co/SPvrJjbdPU Thank you @alexrkonrad Welcome Salesforce Einstein---1st comprehensive AI platform for CRM. https://t.co/MrUk3m4SlV https://t.co/4qz6CXphOn All of my thoughts and prayers are with the people of Italy. May all the residents be protected and safe. Gd Bless. https://t.co/hefc0Fllng To all the friends of @thichnhathanh: I met with the venerable mindfulness teacher today and he was as strong & capable as I have seen him. Salesforce has issued its stakeholder report. We need feedback from all stakeholders on how to move forward. https://t.co/1MOiSl7Jml	Benioff	6	0	4	7	3	28	464	1159	7	78.000000	-0.000256	no change
2	2016-08-26 09:30:00	"Would it be such a bad thing if doing business to change the world became the new normal?" @PledgeOne https://t.co/X2ZN72WdGU Thank you @greentechlady for writing in @FortuneMagazine on @PledgeOne and our 1-1-1 model. https://t.co/kgF8C6TU8R Wonderful to take three days of mindfulness with @thichnhathanh and the monastics at @plumvillageom. Coming home. https://t.co/LyiY521IxZ Thank you @greentechlady and @FortuneMagazine for introducing Salesforce Einstein. https://t.co/tr4V0Dd3fq Thank you @POTUS @McCauley_Lab @SenBrianSchatz @OceanElders for expanding #Papahanaumokuakea! https://t.co/EJAKhANQhq Mahalo Malama Aina!	Benioff	12	1	1	1	4	11	102	301	5	80.389999	0.002869	no change
3	2016-08-26 16:00:00	Thank you @Lilly_Ledbetter! https://t.co/expO7Ti9RQ #WomensEqualityDay Thank you @PattyArquette!	Benioff	2	1	0	0	1	9	36	79	1	80.029999	-0.004478	no change
4	2016-08-27 09:30:00	Wake up call from @PMaurerICRC ceo @ICRC now 65M refugees/24 new refugees every minute. How can we help them? https://t.co/OQAgzJKJau Very excited to get to @Dreamforce and reveal our vision for Einstein---our trusted comprehensive AI platform. https://t.co/ndUhNN9xxx	Benioff	3	0	1	1	1	12	77	183	2	80.329999	0.003749	no change
5	2016-08-28 09:30:00	@s_scally https://t.co/owHDTlfoJH @codyrigsby what song was at the end of the ride this am it was a synthetic version of "remind me of someone I used to know" in last 5 mins Salesforce Einstein is Artitificial Intelligence for Everyone. Coming @Dreamforce. https://t.co/3vCLZdbdlX	Benioff	2	0	2	2	0	14	255	323	3	80.269999	0.003459	no change
6	2016-08-30 09:30:00	So sad Gene Wilder has passed on--one of my favorite actors of all time. Thank you @ConanOBrien for this interview. https://t.co/b6ky8uWnbs	Benioff	1	0	0	0	1	1	24	131	1	80.000000	0.001001	no change
7	2016-08-31 16:00:00	Waiting @iamplusofficial @iamwill @bep new video #WHEREISTHELOVE tonight. Lets get Will to come back to @Dreamforce? https://t.co/8yyXyVM3mz	Benioff	4	1	1	1	0	0	7	44	1	79.419998	-0.007374	no change
8	2016-09-01 09:30:00	Congratulations @iamwill! https://t.co/TTI0ecJe2F Thank you @jimcramer allowing us to announce our first $2B quarter! No other top 10 SW company is growing faster! https://t.co/12ySaosm3f Congratulations Salesforce on achieving your first $2B quarter! No other top 10 software company is growing faster! https://t.co/CaldjRMF4h This video is a glimpse into salesforce's Q2 FY17 results announced today and all of our journeys this quarter. https://t.co/VyeJL6dnt0 Congratulations Salesforce on achieving your first $2B quarter! No other top 10 software company is growing faster! https://t.co/ulIZtjs6Of	Benioff	2	0	3	4	2	25	623	1162	5	75.449997	-0.049987	drop
9	2016-09-01 16:00:00	Great to be in Oakland with all the kids at Frick Middle School with @LibbySchaaf @mayoredlee @SFUSD_Supe. https://t.co/hbf7rVltur Thank you @LibbySchaaf for giving us the leadership to once again invest in the children of Oakland. https://t.co/DTeBoxGE9w The change in education that we all want starts with us and our own personal actions in our local public schools. https://t.co/UvXJfZCiWL Excited to expand our @SalesforceOrg public school program to include Oakland as well as SF. @OUSDNews @SFUnified https://t.co/0H7mrFVBVM	Benioff	7	0	1	0	3	15	108	247	4	75.910004	0.006097	no change

Last rows

	date	tweet	username	mentions	video	photos	urls	replies_count	retweets_count	likes_count	number of tweets	price	percent change	bins
1826	2021-05-20 16:00:00	Congrats Jane! ❤️🌲	Benioff	0	0	0	0	3	27	247	1	223.779999	0.003588	no change
1827	2021-05-21 09:30:00	People expect corporations and CEOs to continue addressing the most pressing social and political issues even after the pandemic is over, according to new data. https://t.co/SwpZZKClVf Congrats Jane! Renowned conservationist Jane Goodall has been named as this year’s winner of the prestigious @TempletonPrize, honoring individuals whose life’s work embodies a fusion of science and spirituality. ❤️🌲 READ MORE: https://t.co/3qa0SWPxXY… https://t.co/t3wz9ZWAyo	Benioff	1	1	1	2	20	37	480	2	225.210007	0.006390	no change
1828	2021-05-23 09:30:00	Our 2nd relief aircraft on its way to India! @Salesforce and @vkhosla partnered with @GiveIndia to deliver this 2nd plane of much needed oxygen concentrators. Our hearts continue to be with our brothers & sisters in India. May they all be protected, healed, & blessed! ❤️🇮🇳🙏 https://t.co/eh6JPuz4x1 When former prime minister Indira Gandhi was asked about Bahuguna's movement, she said: "Well, frankly, I don't know all the aims of the movement. But if it is that trees should not be cut, I'm all for it." ❤️🌲 @1t_org https://t.co/xHuFA9k2xl Salesforce is already a Net Zero company. Now, Salesforce & Accenture are showing our customers how to become Net Zero & leaders using our Sustainability Cloud. Why? Because customers want it & employees rally around it. Join us — put Sustainability first: https://t.co/9ZtoFQGWF7 https://t.co/Ubdhp422UM	Benioff	4	2	4	2	100	526	4126	3	225.070002	0.004553	no change
1829	2021-05-24 09:30:00	Where is the Love? Anti-Semitism. Anti-Palestinian. Anti-Asian. Anti-Muslim. Anti-Armenian. Anti-Black. Anti-LGBTQ. Anti-Christian. Anti-Latino. Anti-Jew. Anti-Arab. All are Anti-Love. How do we Love thy neighbor as thyself? May the One that brings Love bring Love to All. ❤️ https://t.co/qkl54ApayH Already weakened health systems across Yemen have been even further devastated by the impact of the COVID pandemic. 16.2M people in the country will face high levels of acute food shortages with an estimated 21,000 children at risk of falling into famine. https://t.co/GEbndOG6bN	Benioff	0	1	1	1	55	149	877	2	225.000000	-0.002306	no change
1830	2021-05-25 09:30:00	Our 2nd relief aircraft has landed in India! @Salesforce & @vkhosla partnered with @GiveIndia to deliver a plane of much needed oxygen concentrators. Our hearts are with our brothers & sisters in India. May they all be protected, healed, & blessed! ❤️🇮🇳🙏 https://t.co/tuXX9Nm3TU	Benioff	3	0	0	1	22	104	843	1	228.000000	0.004450	no change
1831	2021-05-25 16:00:00	For me, meditation and leadership go hand-in-hand. On the newest episode of the "What I've Learned" podcast with @ariannahuff, I open up about how that connection is helping me shape a post-pandemic future: https://t.co/mH5NnkaZWR https://t.co/cnlwCYyG9T @harryhurst @SFDCTowerUK	Benioff	2	1	1	1	6	38	223	2	227.710007	-0.001272	no change
1832	2021-05-26 09:30:00	Welcome back London Ohana! ❤️🇬🇧 Last nights moon was so inspiring. From WSJ today: A ‘super blue blood moon’ in San Francisco, in 2018. A blood moon is when light filtered through Earth’s atmosphere during an eclipse turns the moon a rusty, reddish hue. PHOTO: JOSH EDELSON/AGENCE FRANCE-PRESSE/GETTY IMAGES https://t.co/HuHwwDrluo	Benioff	0	1	1	0	4	48	491	2	228.479996	0.003381	no change
1833	2021-05-26 16:00:00	Congrats @zackparisa @MaxNova90! “Microsoft, which bought more than $3 million worth of the resulting carbon offsets, helped @NCX map U.S. forest data, such as tree species and wood volume, using satellite imagery and computers.” https://t.co/m152NgplGX	Benioff	3	0	0	1	7	18	97	1	229.679993	0.005252	no change
1834	2021-05-27 16:00:00	Our 2nd relief aircraft has landed in India & contents delivered to @giveindia! @Salesforce & @vkhosla landed the plane of much needed oxygen concentrators & oximeters. Our hearts are with our brothers & sisters in India. May they all be protected, healed, & blessed! ❤️🇮🇳🙏 https://t.co/FqAoplDwz5	Benioff	3	1	0	0	34	104	866	1	225.830002	-0.016077	drop
1835	2021-05-28 09:30:00	Wonderful to be on @MadMoneyOnCNBC tonight announcing our record Q1 results. https://t.co/anwxjChHdB It’s happening! @Dreamforce ❤️ https://t.co/7VMP975nwv Here comes Dreamforce 2021! In person! All of our announcements today on @MadMoneyOnCNBC with @jimcramer tonight! 👍 https://t.co/anwxjChHdB Salesforce Growth: 2022 $26B (guidance) 2021 $21.25B 2020 $17.1B 2019 $13.2B 2018 $10.5B 2017 $8.4B 2016 $6.7B 2015 $5.4B 2014 $4.1B Thank you Ohana! ❤️ From 1st Earnings Call Inside. (We’re all vaccinated!) https://t.co/DQAwBNHm2A	Benioff	4	1	1	3	128	407	4397	4	239.220001	0.059292	rise

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Other Symbol

Lowercase Letter

Uppercase Letter

Other Punctuation

Decimal Number

Math Symbol

Format

Space Separator

Dash Punctuation

Currency Symbol

Connector Punctuation

Final Punctuation

Close Punctuation

Open Punctuation

Initial Punctuation

Modifier Symbol

Other Letter

Nonspacing Mark

Modifier Letter

Control

Most occurring scripts

Most frequent character per script

Common

Latin

Inherited

Hangul

Hiragana

Most occurring blocks

Most frequent character per block

ASCII

Punctuation

Enclosed Alphanum Sup

Dingbats

VS

None

Misc Symbols

Latin 1 Sup

Letterlike Symbols

Emoticons

Latin Ext A

Modifier Letters

Compat Jamo

Hiragana

Arrows

Misc Technical

Math Alphanum

Currency Symbols

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block